{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import models\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE=2048\n",
    "EPOCHS=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def evaluate_given_keras_model(model_builder, scheduler=None, folds=5, prefix=\"data_no_features/fold_\", epochs=EPOCHS, batch_size=BATCH_SIZE, thresholds=[0.5],verbose=1):\n",
    "#     auc_scores = []\n",
    "#     all_y_true = []\n",
    "#     all_y_pred_prob = []\n",
    "#     mean_fpr = np.linspace(0, 1, 100)\n",
    "#     tprs = []\n",
    "\n",
    "#     for i in range(folds):\n",
    "#         # Wczytanie danych\n",
    "#         train = pd.read_csv(f\"{prefix}{i+1}_train_no_features.csv\")\n",
    "#         test = pd.read_csv(f\"{prefix}{i+1}_test_no_features.csv\")\n",
    "\n",
    "#         train.iloc[:, 0] = train.iloc[:, 0].apply(lambda x: 1 if x > 1 else x)\n",
    "#         test.iloc[:, 0] = test.iloc[:, 0].apply(lambda x: 1 if x > 1 else x)\n",
    "\n",
    "#         # scaler = MinMaxScaler()\n",
    "#         # train = pd.DataFrame(scaler.fit_transform(train), columns=train.columns)\n",
    "#         # test = pd.DataFrame(scaler.transform(test), columns=test.columns)\n",
    "\n",
    "#         X_train = train.iloc[:, 1:].values\n",
    "#         y_train = train.iloc[: ,0].values\n",
    "\n",
    "#         X_test = test.iloc[:, 1:].values\n",
    "#         y_test = test.iloc[: ,0].values\n",
    "\n",
    "#         model = model_builder(X_train.shape[1])\n",
    "#         callbacks = [scheduler] if scheduler else []\n",
    "\n",
    "#         model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size,\n",
    "#                   verbose=verbose, callbacks=callbacks,validation_data=(X_test,y_test))\n",
    "\n",
    "#         # Predykcja prawdopodobieństw\n",
    "#         y_pred_prob = model.predict(X_test).ravel()\n",
    "#         all_y_pred_prob.extend(y_pred_prob)\n",
    "#         all_y_true.extend(y_test)\n",
    "\n",
    "#         # ROC\n",
    "#         fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
    "#         interp_tpr = np.interp(mean_fpr, fpr, tpr)\n",
    "#         interp_tpr[0] = 0.0\n",
    "#         tprs.append(interp_tpr)\n",
    "#         auc_scores.append(auc(fpr, tpr))\n",
    "\n",
    "#     # Zamiana na array\n",
    "#     all_y_true = np.array(all_y_true)\n",
    "#     all_y_pred_prob = np.array(all_y_pred_prob)\n",
    "\n",
    "#     # Średni AUC\n",
    "#     mean_auc = np.mean(auc_scores)\n",
    "\n",
    "#     # Metryki dla różnych progów\n",
    "#     print(\"\\nWyniki dla różnych wartości threshold:\")\n",
    "#     for thresh in thresholds:\n",
    "#         y_pred = (all_y_pred_prob >= thresh).astype(int)\n",
    "#         cm = confusion_matrix(all_y_true, y_pred)\n",
    "#         tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "#         accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
    "\n",
    "#         precision=  tp / (tp+fp)if (tp+fp) > 0 else 0\n",
    "#         sensitivity = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "#         specificity = tn / (tn + fp) if (tn + fp) > 0 else 0\n",
    "#         F1 = 2 * tp / (2 * tp + fp + fn) if (2 * tp + fp + fn) > 0 else 0\n",
    "\n",
    "#         print(f\"\\nThreshold = {thresh}\")\n",
    "#         print(f\"Accuracy:     {accuracy:.4f}\")\n",
    "#         print(f\"F1:           {F1:.4f}\")\n",
    "#         print(f\"Precision:    {precision:.4f}\")\n",
    "#         print(f\"Sensitivity:  {sensitivity:.4f}\")\n",
    "#         print(f\"Specificity:  {specificity:.4f}\")\n",
    "#         print(f\"TP:           {tp}\")\n",
    "#         print(f\"FP:           {fp}\")\n",
    "#         print(f\"TN:           {tn}\")\n",
    "#         print(f\"FN:           {fn}\")\n",
    "#         print(\"         Pred 1    Pred 0\")\n",
    "#         print(f\"True 1    {tp:4}     {fn:4}\")\n",
    "#         print(f\"True 0    {fp:4}     {tn:4}\")\n",
    "\n",
    "\n",
    "#     print(f\"AUC= {mean_auc:.4f}\")\n",
    "#     # Średnia krzywa ROC\n",
    "#     mean_tpr = np.mean(tprs, axis=0)\n",
    "#     mean_tpr[-1] = 1.0\n",
    "#     plt.figure(figsize=(8, 6))\n",
    "#     plt.plot(mean_fpr, mean_tpr, label=f'ROC (AUC = {mean_auc:.4f})')\n",
    "#     plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
    "#     plt.xlabel('False Positive Rate')\n",
    "#     plt.ylabel('True Positive Rate')\n",
    "#     plt.title('Średnia krzywa ROC (5-fold)')\n",
    "#     plt.legend(loc='lower right')\n",
    "#     plt.grid(True)\n",
    "#     plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def evaluate_given_keras_model(model_builder, scheduler=None, folds=5, prefix=\"data_no_features/fold_\", \n",
    "                                epochs=EPOCHS, batch_size=BATCH_SIZE, thresholds=[0.5], verbose=1):\n",
    "    auc_scores = []\n",
    "    all_y_true = []\n",
    "    all_y_pred_prob = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    tprs = []\n",
    "\n",
    "    fold_preds = []\n",
    "    fold_trues = []\n",
    "\n",
    "    for i in range(folds):\n",
    "        train = pd.read_csv(f\"{prefix}{i+1}_train_no_features.csv\")\n",
    "        test = pd.read_csv(f\"{prefix}{i+1}_test_no_features.csv\")\n",
    "\n",
    "        train.iloc[:, 0] = train.iloc[:, 0].apply(lambda x: 1 if x > 1 else x)\n",
    "        test.iloc[:, 0] = test.iloc[:, 0].apply(lambda x: 1 if x > 1 else x)\n",
    "\n",
    "        X_train = train.iloc[:, 1:].values\n",
    "        y_train = train.iloc[:, 0].values\n",
    "        X_test = test.iloc[:, 1:].values\n",
    "        y_test = test.iloc[:, 0].values\n",
    "\n",
    "        model = model_builder(X_train.shape[1])\n",
    "        callbacks = [scheduler] if scheduler else []\n",
    "\n",
    "        model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size,\n",
    "                  verbose=verbose, callbacks=callbacks, validation_data=(X_test, y_test))\n",
    "\n",
    "        y_pred_prob = model.predict(X_test).ravel()\n",
    "        all_y_pred_prob.extend(y_pred_prob)\n",
    "        all_y_true.extend(y_test)\n",
    "\n",
    "        fold_preds.append(y_pred_prob)\n",
    "        fold_trues.append(y_test)\n",
    "\n",
    "        fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
    "        interp_tpr = np.interp(mean_fpr, fpr, tpr)\n",
    "        interp_tpr[0] = 0.0\n",
    "        tprs.append(interp_tpr)\n",
    "        auc_scores.append(auc(fpr, tpr))\n",
    "\n",
    "    all_y_true = np.array(all_y_true)\n",
    "    all_y_pred_prob = np.array(all_y_pred_prob)\n",
    "    mean_auc = np.mean(auc_scores)\n",
    "\n",
    "    print(\"\\nWyniki dla różnych wartości threshold:\")\n",
    "    for thresh in thresholds:\n",
    "        metryki = {\n",
    "            'acc': [],\n",
    "            'f1': [],\n",
    "            'precision': [],\n",
    "            'sensitivity': [],\n",
    "            'specificity': [],\n",
    "            'mcc': [],\n",
    "            'tp': [],\n",
    "            'fp': [],\n",
    "            'tn': [],\n",
    "            'fn': []\n",
    "        }\n",
    "\n",
    "        for y_true, y_pred_prob in zip(fold_trues, fold_preds):\n",
    "            y_pred = (y_pred_prob >= thresh).astype(int)\n",
    "            cm = confusion_matrix(y_true, y_pred)\n",
    "            tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "            acc = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0\n",
    "            prec = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "            sens = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "            spec = tn / (tn + fp) if (tn + fp) > 0 else 0\n",
    "            f1 = 2 * tp / (2 * tp + fp + fn) if (2 * tp + fp + fn) > 0 else 0\n",
    "\n",
    "            denom = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n",
    "            mcc = ((tp * tn) - (fp * fn)) / denom if denom > 0 else 0\n",
    "\n",
    "            # Zbieranie\n",
    "            metryki['acc'].append(acc)\n",
    "            metryki['f1'].append(f1)\n",
    "            metryki['precision'].append(prec)\n",
    "            metryki['sensitivity'].append(sens)\n",
    "            metryki['specificity'].append(spec)\n",
    "            metryki['mcc'].append(mcc)\n",
    "            metryki['tp'].append(tp)\n",
    "            metryki['fp'].append(fp)\n",
    "            metryki['tn'].append(tn)\n",
    "            metryki['fn'].append(fn)\n",
    "\n",
    "        def mean_std(lst):\n",
    "            return f\"{np.mean(lst):.4f} ± {np.std(lst):.4f}\"\n",
    "\n",
    "        print(f\"\\nThreshold = {thresh}\")\n",
    "        print(f\"Accuracy:     {mean_std(metryki['acc'])}\")\n",
    "        print(f\"F1:           {mean_std(metryki['f1'])}\")\n",
    "        print(f\"Precision:    {mean_std(metryki['precision'])}\")\n",
    "        print(f\"Sensitivity:  {mean_std(metryki['sensitivity'])}\")\n",
    "        print(f\"Specificity:  {mean_std(metryki['specificity'])}\")\n",
    "        print(f\"MCC:          {mean_std(metryki['mcc'])}\")\n",
    "        print(f\"TP:           {mean_std(metryki['tp'])}\")\n",
    "        print(f\"FP:           {mean_std(metryki['fp'])}\")\n",
    "        print(f\"TN:           {mean_std(metryki['tn'])}\")\n",
    "        print(f\"FN:           {mean_std(metryki['fn'])}\")\n",
    "\n",
    "    print(f\"\\nŚredni AUC = {mean_auc:.4f}\")\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.plot(mean_fpr, mean_tpr, label=f'ROC (AUC = {mean_auc:.4f})')\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Średnia krzywa ROC (5-fold)')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DENSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.7484 - loss: 0.6824 - val_accuracy: 0.6520 - val_loss: 0.6317 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.7717 - loss: 0.5248 - val_accuracy: 0.6563 - val_loss: 0.7196 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.7992 - loss: 0.4814 - val_accuracy: 0.7586 - val_loss: 0.5563 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8415 - loss: 0.4059 - val_accuracy: 0.7564 - val_loss: 0.5092 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8454 - loss: 0.3817 - val_accuracy: 0.7683 - val_loss: 0.5056 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.8564 - loss: 0.3520 - val_accuracy: 0.7888 - val_loss: 0.4640 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8643 - loss: 0.3329 - val_accuracy: 0.7475 - val_loss: 0.5775 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8612 - loss: 0.3370 - val_accuracy: 0.7957 - val_loss: 0.4694 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8698 - loss: 0.3128 - val_accuracy: 0.7968 - val_loss: 0.4598 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8739 - loss: 0.3042 - val_accuracy: 0.7968 - val_loss: 0.4673 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8755 - loss: 0.2967 - val_accuracy: 0.8000 - val_loss: 0.4569 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8775 - loss: 0.2907 - val_accuracy: 0.7938 - val_loss: 0.4822 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8788 - loss: 0.2870 - val_accuracy: 0.7979 - val_loss: 0.4646 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8787 - loss: 0.2854 - val_accuracy: 0.7965 - val_loss: 0.4666 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8803 - loss: 0.2815 - val_accuracy: 0.8009 - val_loss: 0.4501 - learning_rate: 3.6788e-04\n",
      "\u001b[1m284/284\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.7513 - loss: 0.6842 - val_accuracy: 0.7197 - val_loss: 0.5900 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.7883 - loss: 0.5089 - val_accuracy: 0.7575 - val_loss: 0.5347 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8308 - loss: 0.4298 - val_accuracy: 0.7675 - val_loss: 0.5088 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8380 - loss: 0.3986 - val_accuracy: 0.7572 - val_loss: 0.5235 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8471 - loss: 0.3719 - val_accuracy: 0.7776 - val_loss: 0.5246 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8639 - loss: 0.3355 - val_accuracy: 0.7821 - val_loss: 0.5177 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8665 - loss: 0.3243 - val_accuracy: 0.7774 - val_loss: 0.5271 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8694 - loss: 0.3182 - val_accuracy: 0.7825 - val_loss: 0.5353 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8734 - loss: 0.3043 - val_accuracy: 0.7750 - val_loss: 0.5499 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8752 - loss: 0.2981 - val_accuracy: 0.7891 - val_loss: 0.5075 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8787 - loss: 0.2923 - val_accuracy: 0.7949 - val_loss: 0.5173 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8777 - loss: 0.2870 - val_accuracy: 0.7932 - val_loss: 0.5153 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8792 - loss: 0.2864 - val_accuracy: 0.7848 - val_loss: 0.5211 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8811 - loss: 0.2834 - val_accuracy: 0.7880 - val_loss: 0.5336 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8817 - loss: 0.2790 - val_accuracy: 0.7950 - val_loss: 0.5282 - learning_rate: 3.6788e-04\n",
      "\u001b[1m280/280\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.7244 - loss: 0.6572 - val_accuracy: 0.6337 - val_loss: 0.6246 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.7762 - loss: 0.5193 - val_accuracy: 0.6695 - val_loss: 0.6397 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8198 - loss: 0.4566 - val_accuracy: 0.7747 - val_loss: 0.4928 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8326 - loss: 0.4194 - val_accuracy: 0.7734 - val_loss: 0.5096 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8501 - loss: 0.3734 - val_accuracy: 0.7850 - val_loss: 0.4924 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8599 - loss: 0.3484 - val_accuracy: 0.7889 - val_loss: 0.4719 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8638 - loss: 0.3375 - val_accuracy: 0.7878 - val_loss: 0.4652 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8642 - loss: 0.3263 - val_accuracy: 0.7900 - val_loss: 0.4720 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8741 - loss: 0.3083 - val_accuracy: 0.7898 - val_loss: 0.4991 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8714 - loss: 0.3038 - val_accuracy: 0.7891 - val_loss: 0.4688 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8719 - loss: 0.3079 - val_accuracy: 0.7947 - val_loss: 0.4622 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8771 - loss: 0.2985 - val_accuracy: 0.7889 - val_loss: 0.5024 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8807 - loss: 0.2865 - val_accuracy: 0.7775 - val_loss: 0.5353 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8768 - loss: 0.2898 - val_accuracy: 0.7943 - val_loss: 0.4650 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8816 - loss: 0.2871 - val_accuracy: 0.7901 - val_loss: 0.4830 - learning_rate: 3.6788e-04\n",
      "\u001b[1m287/287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.7451 - loss: 0.6640 - val_accuracy: 0.6590 - val_loss: 0.6607 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.7821 - loss: 0.5122 - val_accuracy: 0.7312 - val_loss: 0.6061 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8298 - loss: 0.4364 - val_accuracy: 0.7357 - val_loss: 0.5570 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8298 - loss: 0.4210 - val_accuracy: 0.7641 - val_loss: 0.5169 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8507 - loss: 0.3769 - val_accuracy: 0.7814 - val_loss: 0.4898 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8564 - loss: 0.3568 - val_accuracy: 0.7846 - val_loss: 0.4757 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8598 - loss: 0.3452 - val_accuracy: 0.7725 - val_loss: 0.4750 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8611 - loss: 0.3387 - val_accuracy: 0.7944 - val_loss: 0.4507 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8692 - loss: 0.3168 - val_accuracy: 0.7822 - val_loss: 0.4729 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8692 - loss: 0.3152 - val_accuracy: 0.7863 - val_loss: 0.4870 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8697 - loss: 0.3151 - val_accuracy: 0.8020 - val_loss: 0.4280 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8724 - loss: 0.3045 - val_accuracy: 0.8035 - val_loss: 0.4287 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8766 - loss: 0.2956 - val_accuracy: 0.8054 - val_loss: 0.4134 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8787 - loss: 0.2925 - val_accuracy: 0.7936 - val_loss: 0.4607 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8748 - loss: 0.2952 - val_accuracy: 0.8124 - val_loss: 0.4046 - learning_rate: 3.6788e-04\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.6818 - loss: 0.6262 - val_accuracy: 0.6643 - val_loss: 0.6212 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.7895 - loss: 0.5063 - val_accuracy: 0.7617 - val_loss: 0.5218 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8282 - loss: 0.4326 - val_accuracy: 0.7593 - val_loss: 0.5536 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.8416 - loss: 0.3942 - val_accuracy: 0.7814 - val_loss: 0.5150 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8586 - loss: 0.3565 - val_accuracy: 0.7815 - val_loss: 0.5097 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8612 - loss: 0.3450 - val_accuracy: 0.7843 - val_loss: 0.5076 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8679 - loss: 0.3290 - val_accuracy: 0.7852 - val_loss: 0.5427 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8692 - loss: 0.3173 - val_accuracy: 0.7488 - val_loss: 0.5679 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8662 - loss: 0.3246 - val_accuracy: 0.7838 - val_loss: 0.5274 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8769 - loss: 0.2963 - val_accuracy: 0.7848 - val_loss: 0.5603 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8782 - loss: 0.2900 - val_accuracy: 0.7854 - val_loss: 0.5611 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8817 - loss: 0.2834 - val_accuracy: 0.7809 - val_loss: 0.5802 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8776 - loss: 0.2866 - val_accuracy: 0.7793 - val_loss: 0.6362 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.8813 - loss: 0.2841 - val_accuracy: 0.7871 - val_loss: 0.5741 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.8849 - loss: 0.2728 - val_accuracy: 0.7876 - val_loss: 0.5754 - learning_rate: 3.6788e-04\n",
      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "\n",
      "Wyniki dla różnych wartości threshold:\n",
      "\n",
      "Threshold = 0.3\n",
      "Accuracy:     0.7802 ± 0.0110\n",
      "F1:           0.8394 ± 0.0057\n",
      "Precision:    0.7530 ± 0.0138\n",
      "Sensitivity:  0.9487 ± 0.0100\n",
      "Specificity:  0.5215 ± 0.0420\n",
      "MCC:          0.5410 ± 0.0209\n",
      "TP:           5217.4000 ± 102.7124\n",
      "FP:           1715.2000 ± 150.1338\n",
      "TN:           1869.2000 ± 152.2595\n",
      "FN:           281.8000 ± 53.6298\n",
      "\n",
      "Threshold = 0.5\n",
      "Accuracy:     0.7972 ± 0.0089\n",
      "F1:           0.8406 ± 0.0057\n",
      "Precision:    0.8027 ± 0.0181\n",
      "Sensitivity:  0.8834 ± 0.0231\n",
      "Specificity:  0.6648 ± 0.0494\n",
      "MCC:          0.5699 ± 0.0197\n",
      "TP:           4858.8000 ± 164.0188\n",
      "FP:           1201.6000 ± 177.7466\n",
      "TN:           2382.8000 ± 177.4017\n",
      "FN:           640.4000 ± 124.8497\n",
      "\n",
      "Threshold = 0.7\n",
      "Accuracy:     0.7774 ± 0.0080\n",
      "F1:           0.8075 ± 0.0096\n",
      "Precision:    0.8480 ± 0.0218\n",
      "Sensitivity:  0.7722 ± 0.0311\n",
      "Specificity:  0.7853 ± 0.0444\n",
      "MCC:          0.5494 ± 0.0189\n",
      "TP:           4247.2000 ± 194.0138\n",
      "FP:           769.6000 ± 158.8894\n",
      "TN:           2814.8000 ± 161.9103\n",
      "FN:           1252.0000 ± 168.0238\n",
      "\n",
      "Średni AUC = 0.8540\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def custom_scheduler(epoch, lr):\n",
    "    if epoch < 5:\n",
    "        return lr\n",
    "    else:\n",
    "        return float(lr * tf.math.exp(-0.1))\n",
    "\n",
    "def build_model(input_dim):\n",
    "    model = models.Sequential([\n",
    "        layers.Input(shape=(input_dim,)),\n",
    "    layers.Dense(1024, activation='relu'), \n",
    "    layers.Dense(512, activation='relu'),\n",
    "    layers.Dense(256, activation='relu'),\n",
    "    layers.Dense(128, activation='relu'), \n",
    "    layers.Dense(1, activation='sigmoid') \n",
    "    ])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "scheduler = LearningRateScheduler(custom_scheduler)\n",
    "evaluate_given_keras_model(build_model,thresholds=[0.3, 0.5, 0.7], scheduler=scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 518ms/step - accuracy: 0.7323 - loss: 0.6016 - val_accuracy: 0.6044 - val_loss: 0.6729 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 507ms/step - accuracy: 0.7776 - loss: 0.4745 - val_accuracy: 0.7362 - val_loss: 0.6131 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 512ms/step - accuracy: 0.8324 - loss: 0.4172 - val_accuracy: 0.7417 - val_loss: 0.5964 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 518ms/step - accuracy: 0.8407 - loss: 0.3979 - val_accuracy: 0.7601 - val_loss: 0.5519 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 525ms/step - accuracy: 0.8496 - loss: 0.3710 - val_accuracy: 0.7665 - val_loss: 0.5312 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 545ms/step - accuracy: 0.8531 - loss: 0.3570 - val_accuracy: 0.7703 - val_loss: 0.5226 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 550ms/step - accuracy: 0.8557 - loss: 0.3482 - val_accuracy: 0.7778 - val_loss: 0.4978 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 541ms/step - accuracy: 0.8584 - loss: 0.3340 - val_accuracy: 0.7640 - val_loss: 0.5063 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 561ms/step - accuracy: 0.8574 - loss: 0.3387 - val_accuracy: 0.7730 - val_loss: 0.5137 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 537ms/step - accuracy: 0.8589 - loss: 0.3288 - val_accuracy: 0.7791 - val_loss: 0.4795 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 522ms/step - accuracy: 0.8624 - loss: 0.3229 - val_accuracy: 0.7761 - val_loss: 0.5004 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 535ms/step - accuracy: 0.8678 - loss: 0.3088 - val_accuracy: 0.7826 - val_loss: 0.4804 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 533ms/step - accuracy: 0.8670 - loss: 0.3110 - val_accuracy: 0.7845 - val_loss: 0.4718 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 525ms/step - accuracy: 0.8699 - loss: 0.3066 - val_accuracy: 0.7859 - val_loss: 0.4678 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 559ms/step - accuracy: 0.8693 - loss: 0.3042 - val_accuracy: 0.7855 - val_loss: 0.4787 - learning_rate: 3.6788e-04\n",
      "\u001b[1m284/284\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.7522 - loss: 0.5857 - val_accuracy: 0.6385 - val_loss: 0.6533 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 957ms/step - accuracy: 0.7974 - loss: 0.4602 - val_accuracy: 0.7357 - val_loss: 0.5777 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.8365 - loss: 0.4146 - val_accuracy: 0.7672 - val_loss: 0.5214 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.8456 - loss: 0.3875 - val_accuracy: 0.7634 - val_loss: 0.5157 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 786ms/step - accuracy: 0.8493 - loss: 0.3699 - val_accuracy: 0.7775 - val_loss: 0.4862 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 527ms/step - accuracy: 0.8561 - loss: 0.3500 - val_accuracy: 0.7908 - val_loss: 0.4710 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 703ms/step - accuracy: 0.8575 - loss: 0.3448 - val_accuracy: 0.7880 - val_loss: 0.4669 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 989ms/step - accuracy: 0.8614 - loss: 0.3343 - val_accuracy: 0.7621 - val_loss: 0.5106 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 815ms/step - accuracy: 0.8576 - loss: 0.3396 - val_accuracy: 0.7814 - val_loss: 0.4730 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 576ms/step - accuracy: 0.8623 - loss: 0.3273 - val_accuracy: 0.7842 - val_loss: 0.4572 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 583ms/step - accuracy: 0.8619 - loss: 0.3301 - val_accuracy: 0.7763 - val_loss: 0.4776 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 556ms/step - accuracy: 0.8615 - loss: 0.3291 - val_accuracy: 0.7921 - val_loss: 0.4523 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 571ms/step - accuracy: 0.8650 - loss: 0.3193 - val_accuracy: 0.7830 - val_loss: 0.4658 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 559ms/step - accuracy: 0.8655 - loss: 0.3177 - val_accuracy: 0.7931 - val_loss: 0.4500 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 523ms/step - accuracy: 0.8633 - loss: 0.3181 - val_accuracy: 0.7880 - val_loss: 0.4541 - learning_rate: 3.6788e-04\n",
      "\u001b[1m280/280\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.7224 - loss: 0.6019 - val_accuracy: 0.6548 - val_loss: 0.6373 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 760ms/step - accuracy: 0.7805 - loss: 0.4767 - val_accuracy: 0.7323 - val_loss: 0.6069 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 557ms/step - accuracy: 0.8320 - loss: 0.4157 - val_accuracy: 0.7630 - val_loss: 0.5496 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 546ms/step - accuracy: 0.8484 - loss: 0.3786 - val_accuracy: 0.7756 - val_loss: 0.5156 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 553ms/step - accuracy: 0.8557 - loss: 0.3503 - val_accuracy: 0.7802 - val_loss: 0.5002 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 546ms/step - accuracy: 0.8574 - loss: 0.3375 - val_accuracy: 0.7875 - val_loss: 0.4699 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 581ms/step - accuracy: 0.8597 - loss: 0.3278 - val_accuracy: 0.7832 - val_loss: 0.4897 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 550ms/step - accuracy: 0.8665 - loss: 0.3104 - val_accuracy: 0.7819 - val_loss: 0.5006 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 549ms/step - accuracy: 0.8669 - loss: 0.3080 - val_accuracy: 0.7887 - val_loss: 0.4666 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 550ms/step - accuracy: 0.8683 - loss: 0.2999 - val_accuracy: 0.7842 - val_loss: 0.4977 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 547ms/step - accuracy: 0.8705 - loss: 0.2942 - val_accuracy: 0.7900 - val_loss: 0.4685 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 545ms/step - accuracy: 0.8743 - loss: 0.2855 - val_accuracy: 0.7837 - val_loss: 0.4574 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 548ms/step - accuracy: 0.8766 - loss: 0.2880 - val_accuracy: 0.7938 - val_loss: 0.4589 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 545ms/step - accuracy: 0.8780 - loss: 0.2792 - val_accuracy: 0.7956 - val_loss: 0.4574 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 545ms/step - accuracy: 0.8796 - loss: 0.2789 - val_accuracy: 0.7960 - val_loss: 0.4570 - learning_rate: 3.6788e-04\n",
      "\u001b[1m287/287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 554ms/step - accuracy: 0.7510 - loss: 0.6021 - val_accuracy: 0.6050 - val_loss: 0.6593 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 544ms/step - accuracy: 0.7540 - loss: 0.4690 - val_accuracy: 0.7217 - val_loss: 0.6246 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 554ms/step - accuracy: 0.8132 - loss: 0.4347 - val_accuracy: 0.7452 - val_loss: 0.5985 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 540ms/step - accuracy: 0.8442 - loss: 0.4029 - val_accuracy: 0.7554 - val_loss: 0.5921 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 559ms/step - accuracy: 0.8459 - loss: 0.3871 - val_accuracy: 0.7697 - val_loss: 0.5255 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 548ms/step - accuracy: 0.8597 - loss: 0.3522 - val_accuracy: 0.7679 - val_loss: 0.5076 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 554ms/step - accuracy: 0.8581 - loss: 0.3473 - val_accuracy: 0.7702 - val_loss: 0.5042 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 868ms/step - accuracy: 0.8630 - loss: 0.3383 - val_accuracy: 0.7714 - val_loss: 0.5083 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 838ms/step - accuracy: 0.8654 - loss: 0.3307 - val_accuracy: 0.7741 - val_loss: 0.4934 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 553ms/step - accuracy: 0.8641 - loss: 0.3292 - val_accuracy: 0.7584 - val_loss: 0.5137 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 538ms/step - accuracy: 0.8591 - loss: 0.3386 - val_accuracy: 0.7726 - val_loss: 0.4880 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 540ms/step - accuracy: 0.8643 - loss: 0.3268 - val_accuracy: 0.7737 - val_loss: 0.5006 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 545ms/step - accuracy: 0.8658 - loss: 0.3218 - val_accuracy: 0.7735 - val_loss: 0.4905 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 522ms/step - accuracy: 0.8688 - loss: 0.3160 - val_accuracy: 0.7731 - val_loss: 0.4790 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 522ms/step - accuracy: 0.8670 - loss: 0.3165 - val_accuracy: 0.7731 - val_loss: 0.4717 - learning_rate: 3.6788e-04\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 524ms/step - accuracy: 0.7224 - loss: 0.6018 - val_accuracy: 0.6171 - val_loss: 0.6570 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 520ms/step - accuracy: 0.7704 - loss: 0.4809 - val_accuracy: 0.7455 - val_loss: 0.5876 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 523ms/step - accuracy: 0.8264 - loss: 0.4293 - val_accuracy: 0.7390 - val_loss: 0.6196 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 517ms/step - accuracy: 0.8382 - loss: 0.4044 - val_accuracy: 0.7680 - val_loss: 0.5145 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 525ms/step - accuracy: 0.8487 - loss: 0.3731 - val_accuracy: 0.7810 - val_loss: 0.4935 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 529ms/step - accuracy: 0.8522 - loss: 0.3566 - val_accuracy: 0.7831 - val_loss: 0.4831 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 520ms/step - accuracy: 0.8562 - loss: 0.3452 - val_accuracy: 0.7834 - val_loss: 0.4709 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 523ms/step - accuracy: 0.8577 - loss: 0.3392 - val_accuracy: 0.7896 - val_loss: 0.4723 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 554ms/step - accuracy: 0.8614 - loss: 0.3289 - val_accuracy: 0.7900 - val_loss: 0.4690 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 544ms/step - accuracy: 0.8638 - loss: 0.3216 - val_accuracy: 0.7846 - val_loss: 0.4804 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 547ms/step - accuracy: 0.8653 - loss: 0.3170 - val_accuracy: 0.7916 - val_loss: 0.4634 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 549ms/step - accuracy: 0.8652 - loss: 0.3139 - val_accuracy: 0.7920 - val_loss: 0.4635 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 552ms/step - accuracy: 0.8673 - loss: 0.3061 - val_accuracy: 0.7836 - val_loss: 0.4818 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 555ms/step - accuracy: 0.8649 - loss: 0.3118 - val_accuracy: 0.7894 - val_loss: 0.4498 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 554ms/step - accuracy: 0.8691 - loss: 0.3067 - val_accuracy: 0.7928 - val_loss: 0.4561 - learning_rate: 3.6788e-04\n",
      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
      "\n",
      "Wyniki dla różnych wartości threshold:\n",
      "\n",
      "Threshold = 0.3\n",
      "Accuracy:     0.7677 ± 0.0065\n",
      "F1:           0.8326 ± 0.0043\n",
      "Precision:    0.7387 ± 0.0086\n",
      "Sensitivity:  0.9540 ± 0.0108\n",
      "Specificity:  0.4821 ± 0.0244\n",
      "MCC:          0.5165 ± 0.0116\n",
      "TP:           5246.0000 ± 72.5341\n",
      "FP:           1856.2000 ± 81.1847\n",
      "TN:           1728.2000 ± 93.4803\n",
      "FN:           253.2000 ± 60.2740\n",
      "\n",
      "Threshold = 0.5\n",
      "Accuracy:     0.7871 ± 0.0079\n",
      "F1:           0.8328 ± 0.0082\n",
      "Precision:    0.7934 ± 0.0084\n",
      "Sensitivity:  0.8768 ± 0.0207\n",
      "Specificity:  0.6495 ± 0.0214\n",
      "MCC:          0.5473 ± 0.0155\n",
      "TP:           4821.4000 ± 111.1118\n",
      "FP:           1256.2000 ± 73.5701\n",
      "TN:           2328.2000 ± 83.1514\n",
      "FN:           677.8000 ± 116.2384\n",
      "\n",
      "Threshold = 0.7\n",
      "Accuracy:     0.7680 ± 0.0115\n",
      "F1:           0.7972 ± 0.0138\n",
      "Precision:    0.8461 ± 0.0087\n",
      "Sensitivity:  0.7543 ± 0.0277\n",
      "Specificity:  0.7891 ± 0.0188\n",
      "MCC:          0.5335 ± 0.0172\n",
      "TP:           4147.4000 ± 133.4190\n",
      "FP:           756.0000 ± 66.9059\n",
      "TN:           2828.4000 ± 71.0397\n",
      "FN:           1351.8000 ± 160.5109\n",
      "\n",
      "Średni AUC = 0.8548\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def custom_scheduler(epoch, lr):\n",
    "    if epoch < 5:\n",
    "        return lr\n",
    "    else:\n",
    "        return float(lr * tf.math.exp(-0.1))\n",
    "\n",
    "def build_model(input_dim):\n",
    "    model = models.Sequential([\n",
    "    layers.Input(shape=(input_dim,)),\n",
    "    layers.Reshape((input_dim, 1)), \n",
    "    layers.Conv1D(64, (3), activation='relu'),\n",
    "    layers.MaxPooling1D((2)),\n",
    "    layers.Conv1D(32, (3), activation='relu'),\n",
    "    layers.MaxPooling1D((2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "scheduler = LearningRateScheduler(custom_scheduler)\n",
    "evaluate_given_keras_model(build_model,thresholds=[0.3, 0.5, 0.7], scheduler=scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 1s/step - accuracy: 0.7526 - loss: 0.6268 - val_accuracy: 0.6036 - val_loss: 0.7194 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 1s/step - accuracy: 0.7491 - loss: 0.5620 - val_accuracy: 0.6036 - val_loss: 0.7038 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 1s/step - accuracy: 0.7486 - loss: 0.5407 - val_accuracy: 0.6036 - val_loss: 0.7633 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7503 - loss: 0.5338 - val_accuracy: 0.6036 - val_loss: 0.7119 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 1s/step - accuracy: 0.7529 - loss: 0.5150 - val_accuracy: 0.6036 - val_loss: 0.8068 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 1s/step - accuracy: 0.7508 - loss: 0.5208 - val_accuracy: 0.6037 - val_loss: 0.6959 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 1s/step - accuracy: 0.7558 - loss: 0.4889 - val_accuracy: 0.6300 - val_loss: 0.6418 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7470 - loss: 0.5032 - val_accuracy: 0.6034 - val_loss: 0.7347 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7480 - loss: 0.5360 - val_accuracy: 0.6038 - val_loss: 0.6653 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7580 - loss: 0.4902 - val_accuracy: 0.6036 - val_loss: 0.7727 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7501 - loss: 0.5285 - val_accuracy: 0.6129 - val_loss: 0.6712 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7598 - loss: 0.4766 - val_accuracy: 0.6466 - val_loss: 0.6453 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7819 - loss: 0.4589 - val_accuracy: 0.6968 - val_loss: 0.6302 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7870 - loss: 0.4497 - val_accuracy: 0.6859 - val_loss: 0.6383 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.7996 - loss: 0.4385 - val_accuracy: 0.6923 - val_loss: 0.6576 - learning_rate: 3.6788e-04\n",
      "\u001b[1m284/284\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 33ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 1s/step - accuracy: 0.7501 - loss: 0.6215 - val_accuracy: 0.6032 - val_loss: 0.7158 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7518 - loss: 0.5605"
     ]
    }
   ],
   "source": [
    "def custom_scheduler(epoch, lr):\n",
    "    if epoch < 5:\n",
    "        return lr\n",
    "    else:\n",
    "        return float(lr * tf.math.exp(-0.1))\n",
    "\n",
    "def build_model(input_dim):\n",
    "    model = models.Sequential([\n",
    "    layers.Input(shape=(input_dim,)),\n",
    "    layers.Reshape((input_dim, 1)), \n",
    "    layers.Conv1D(64, (3), activation='relu'),\n",
    "    layers.MaxPooling1D((2)),\n",
    "    layers.Conv1D(32, (3), activation='relu'),\n",
    "    layers.MaxPooling1D((2)),\n",
    "    # layers.Flatten(),\n",
    "    layers.LSTM(64, return_sequences=True),\n",
    "    layers.LSTM(32, return_sequences=False),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "scheduler = LearningRateScheduler(custom_scheduler)\n",
    "evaluate_given_keras_model(build_model,thresholds=[0.3, 0.5, 0.7], scheduler=scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 9s/step - accuracy: 0.7454 - loss: 0.6152 - val_accuracy: 0.7429 - val_loss: 0.5691 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m229s\u001b[0m 9s/step - accuracy: 0.7458 - loss: 0.5649 - val_accuracy: 0.7429 - val_loss: 0.5558 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m233s\u001b[0m 9s/step - accuracy: 0.7484 - loss: 0.5528 - val_accuracy: 0.7429 - val_loss: 0.5673 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m236s\u001b[0m 9s/step - accuracy: 0.7487 - loss: 0.5576 - val_accuracy: 0.7512 - val_loss: 0.4465 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m238s\u001b[0m 9s/step - accuracy: 0.7512 - loss: 0.5357 - val_accuracy: 0.7429 - val_loss: 0.5776 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m239s\u001b[0m 9s/step - accuracy: 0.7472 - loss: 0.5682 - val_accuracy: 0.7429 - val_loss: 0.5701 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 9s/step - accuracy: 0.7481 - loss: 0.5646 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m239s\u001b[0m 9s/step - accuracy: 0.7484 - loss: 0.5641 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 9s/step - accuracy: 0.7440 - loss: 0.5688 - val_accuracy: 0.7429 - val_loss: 0.5701 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 9s/step - accuracy: 0.7457 - loss: 0.5670 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m247s\u001b[0m 10s/step - accuracy: 0.7469 - loss: 0.5657 - val_accuracy: 0.7429 - val_loss: 0.5701 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m251s\u001b[0m 10s/step - accuracy: 0.7452 - loss: 0.5676 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m251s\u001b[0m 10s/step - accuracy: 0.7456 - loss: 0.5671 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 10s/step - accuracy: 0.7450 - loss: 0.5678 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 10s/step - accuracy: 0.7448 - loss: 0.5679 - val_accuracy: 0.7429 - val_loss: 0.5700 - learning_rate: 3.6788e-04\n",
      "\u001b[1m398/398\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 160ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 10s/step - accuracy: 0.6775 - loss: 0.6230 - val_accuracy: 0.7443 - val_loss: 0.5685 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 9s/step - accuracy: 0.7465 - loss: 0.5661 - val_accuracy: 0.7443 - val_loss: 0.5676 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 9s/step - accuracy: 0.7467 - loss: 0.5631 - val_accuracy: 0.7443 - val_loss: 0.5686 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 9s/step - accuracy: 0.7489 - loss: 0.5592 - val_accuracy: 0.7432 - val_loss: 0.5239 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 9s/step - accuracy: 0.7579 - loss: 0.5037 - val_accuracy: 0.8131 - val_loss: 0.4034 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 9s/step - accuracy: 0.8328 - loss: 0.4100 - val_accuracy: 0.7442 - val_loss: 0.5412 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m247s\u001b[0m 10s/step - accuracy: 0.7563 - loss: 0.4978 - val_accuracy: 0.8436 - val_loss: 0.4650 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 10s/step - accuracy: 0.7685 - loss: 0.4893 - val_accuracy: 0.7361 - val_loss: 0.5631 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m253s\u001b[0m 10s/step - accuracy: 0.6815 - loss: 0.6066 - val_accuracy: 0.7443 - val_loss: 0.5758 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 10s/step - accuracy: 0.7474 - loss: 0.5674 - val_accuracy: 0.7443 - val_loss: 0.5689 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 10s/step - accuracy: 0.7480 - loss: 0.5650 - val_accuracy: 0.7443 - val_loss: 0.5685 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 10s/step - accuracy: 0.7435 - loss: 0.5693 - val_accuracy: 0.7443 - val_loss: 0.5684 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m269s\u001b[0m 10s/step - accuracy: 0.7460 - loss: 0.5666 - val_accuracy: 0.7443 - val_loss: 0.5683 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m269s\u001b[0m 10s/step - accuracy: 0.7443 - loss: 0.5683 - val_accuracy: 0.7443 - val_loss: 0.5681 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 11s/step - accuracy: 0.7468 - loss: 0.5654 - val_accuracy: 0.7443 - val_loss: 0.5675 - learning_rate: 3.6788e-04\n",
      "\u001b[1m401/401\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 167ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m334s\u001b[0m 13s/step - accuracy: 0.6917 - loss: 0.6179 - val_accuracy: 0.7565 - val_loss: 0.5526 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m309s\u001b[0m 12s/step - accuracy: 0.7425 - loss: 0.5651 - val_accuracy: 0.7565 - val_loss: 0.5444 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m314s\u001b[0m 12s/step - accuracy: 0.7407 - loss: 0.5519 - val_accuracy: 0.7565 - val_loss: 0.5638 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m316s\u001b[0m 12s/step - accuracy: 0.7424 - loss: 0.5727 - val_accuracy: 0.7565 - val_loss: 0.5549 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m315s\u001b[0m 12s/step - accuracy: 0.7418 - loss: 0.5712 - val_accuracy: 0.7565 - val_loss: 0.5548 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 12s/step - accuracy: 0.7432 - loss: 0.5697 - val_accuracy: 0.7565 - val_loss: 0.5551 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m321s\u001b[0m 12s/step - accuracy: 0.7426 - loss: 0.5698 - val_accuracy: 0.7565 - val_loss: 0.5546 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m322s\u001b[0m 12s/step - accuracy: 0.7425 - loss: 0.5696 - val_accuracy: 0.7565 - val_loss: 0.5522 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m323s\u001b[0m 12s/step - accuracy: 0.7443 - loss: 0.5666 - val_accuracy: 0.7565 - val_loss: 0.5552 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m322s\u001b[0m 12s/step - accuracy: 0.7395 - loss: 0.5743 - val_accuracy: 0.7565 - val_loss: 0.5549 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m320s\u001b[0m 12s/step - accuracy: 0.7427 - loss: 0.5704 - val_accuracy: 0.7565 - val_loss: 0.5552 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m319s\u001b[0m 12s/step - accuracy: 0.7421 - loss: 0.5706 - val_accuracy: 0.7565 - val_loss: 0.5551 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 12s/step - accuracy: 0.7431 - loss: 0.5694 - val_accuracy: 0.7565 - val_loss: 0.5550 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 12s/step - accuracy: 0.7443 - loss: 0.5681 - val_accuracy: 0.7565 - val_loss: 0.5548 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m320s\u001b[0m 12s/step - accuracy: 0.7435 - loss: 0.5683 - val_accuracy: 0.7565 - val_loss: 0.5544 - learning_rate: 3.6788e-04\n",
      "\u001b[1m414/414\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 183ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m285s\u001b[0m 11s/step - accuracy: 0.7486 - loss: 0.6140 - val_accuracy: 0.7449 - val_loss: 0.5661 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 10s/step - accuracy: 0.7457 - loss: 0.5623 - val_accuracy: 0.7449 - val_loss: 0.5603 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 10s/step - accuracy: 0.7471 - loss: 0.5428 - val_accuracy: 0.7449 - val_loss: 0.5676 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 10s/step - accuracy: 0.7464 - loss: 0.5691 - val_accuracy: 0.7449 - val_loss: 0.5675 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 10s/step - accuracy: 0.7436 - loss: 0.5690 - val_accuracy: 0.7449 - val_loss: 0.5666 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 10s/step - accuracy: 0.7466 - loss: 0.5611 - val_accuracy: 0.7527 - val_loss: 0.5017 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 10s/step - accuracy: 0.7731 - loss: 0.4557 - val_accuracy: 0.8795 - val_loss: 0.3178 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 10s/step - accuracy: 0.8112 - loss: 0.4678 - val_accuracy: 0.7449 - val_loss: 0.5800 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 10s/step - accuracy: 0.7486 - loss: 0.5728 - val_accuracy: 0.7449 - val_loss: 0.5675 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 10s/step - accuracy: 0.7413 - loss: 0.5712 - val_accuracy: 0.7449 - val_loss: 0.5661 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 10s/step - accuracy: 0.7456 - loss: 0.5651 - val_accuracy: 0.7449 - val_loss: 0.5653 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 10s/step - accuracy: 0.7455 - loss: 0.5643 - val_accuracy: 0.7449 - val_loss: 0.5638 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 10s/step - accuracy: 0.7481 - loss: 0.5595 - val_accuracy: 0.7449 - val_loss: 0.5602 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 10s/step - accuracy: 0.7480 - loss: 0.5548 - val_accuracy: 0.7449 - val_loss: 0.5531 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 10s/step - accuracy: 0.7460 - loss: 0.5497 - val_accuracy: 0.7449 - val_loss: 0.5458 - learning_rate: 3.6788e-04\n",
      "\u001b[1m402/402\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 163ms/step\n",
      "Epoch 1/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m312s\u001b[0m 12s/step - accuracy: 0.6749 - loss: 0.6182 - val_accuracy: 0.7488 - val_loss: 0.5624 - learning_rate: 0.0010\n",
      "Epoch 2/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 11s/step - accuracy: 0.7459 - loss: 0.5620 - val_accuracy: 0.7488 - val_loss: 0.5521 - learning_rate: 0.0010\n",
      "Epoch 3/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m275s\u001b[0m 11s/step - accuracy: 0.7458 - loss: 0.5519 - val_accuracy: 0.7488 - val_loss: 0.5505 - learning_rate: 0.0010\n",
      "Epoch 4/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 11s/step - accuracy: 0.7422 - loss: 0.5412 - val_accuracy: 0.7488 - val_loss: 0.5173 - learning_rate: 0.0010\n",
      "Epoch 5/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m283s\u001b[0m 11s/step - accuracy: 0.7401 - loss: 0.5068 - val_accuracy: 0.7488 - val_loss: 0.5268 - learning_rate: 0.0010\n",
      "Epoch 6/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m286s\u001b[0m 11s/step - accuracy: 0.7480 - loss: 0.5039 - val_accuracy: 0.7630 - val_loss: 0.4601 - learning_rate: 9.0484e-04\n",
      "Epoch 7/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m285s\u001b[0m 11s/step - accuracy: 0.7611 - loss: 0.4485 - val_accuracy: 0.7505 - val_loss: 0.5578 - learning_rate: 8.1873e-04\n",
      "Epoch 8/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m289s\u001b[0m 11s/step - accuracy: 0.7781 - loss: 0.4690 - val_accuracy: 0.8812 - val_loss: 0.3725 - learning_rate: 7.4082e-04\n",
      "Epoch 9/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m290s\u001b[0m 11s/step - accuracy: 0.8585 - loss: 0.3764 - val_accuracy: 0.8770 - val_loss: 0.3431 - learning_rate: 6.7032e-04\n",
      "Epoch 10/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m291s\u001b[0m 11s/step - accuracy: 0.8632 - loss: 0.3540 - val_accuracy: 0.4989 - val_loss: 0.7342 - learning_rate: 6.0653e-04\n",
      "Epoch 11/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m293s\u001b[0m 11s/step - accuracy: 0.7110 - loss: 0.5206 - val_accuracy: 0.8626 - val_loss: 0.3740 - learning_rate: 5.4881e-04\n",
      "Epoch 12/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m295s\u001b[0m 11s/step - accuracy: 0.8780 - loss: 0.3616 - val_accuracy: 0.8985 - val_loss: 0.3340 - learning_rate: 4.9659e-04\n",
      "Epoch 13/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m300s\u001b[0m 12s/step - accuracy: 0.8840 - loss: 0.3344 - val_accuracy: 0.7881 - val_loss: 0.5435 - learning_rate: 4.4933e-04\n",
      "Epoch 14/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m306s\u001b[0m 12s/step - accuracy: 0.7806 - loss: 0.4535 - val_accuracy: 0.7337 - val_loss: 0.5211 - learning_rate: 4.0657e-04\n",
      "Epoch 15/15\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m304s\u001b[0m 12s/step - accuracy: 0.7634 - loss: 0.4431 - val_accuracy: 0.8677 - val_loss: 0.3502 - learning_rate: 3.6788e-04\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 165ms/step\n",
      "\n",
      "Wyniki dla różnych wartości threshold:\n",
      "\n",
      "Threshold = 0.3\n",
      "Accuracy:     0.7496\n",
      "F1:           0.8565\n",
      "Precision:    0.7493\n",
      "Sensitivity:  0.9995\n",
      "Specificity:  0.0097\n",
      "TP:           48357\n",
      "FP:           16182\n",
      "TN:           158\n",
      "FN:           25\n",
      "         Pred 1    Pred 0\n",
      "True 1    48357       25\n",
      "True 0    16182      158\n",
      "\n",
      "Threshold = 0.5\n",
      "Accuracy:     0.7715\n",
      "F1:           0.8647\n",
      "Precision:    0.7756\n",
      "Sensitivity:  0.9770\n",
      "Specificity:  0.1633\n",
      "TP:           47267\n",
      "FP:           13672\n",
      "TN:           2668\n",
      "FN:           1115\n",
      "         Pred 1    Pred 0\n",
      "True 1    47267     1115\n",
      "True 0    13672     2668\n",
      "\n",
      "Threshold = 0.7\n",
      "Accuracy:     0.7587\n",
      "F1:           0.8527\n",
      "Precision:    0.7843\n",
      "Sensitivity:  0.9342\n",
      "Specificity:  0.2392\n",
      "TP:           45197\n",
      "FP:           12431\n",
      "TN:           3909\n",
      "FN:           3185\n",
      "         Pred 1    Pred 0\n",
      "True 1    45197     3185\n",
      "True 0    12431     3909\n",
      "AUC= 0.6948\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def custom_scheduler(epoch, lr):\n",
    "    if epoch < 5:\n",
    "        return lr\n",
    "    else:\n",
    "        return float(lr * tf.math.exp(-0.1))\n",
    "\n",
    "def build_model(input_dim):\n",
    "    model = models.Sequential([\n",
    "    layers.Input(shape=(input_dim,)),\n",
    "    layers.Reshape((input_dim, 1)), \n",
    "    layers.LSTM(128, return_sequences=True),\n",
    "    layers.LSTM(64, return_sequences=True),\n",
    "    layers.LSTM(32, return_sequences=False),\n",
    "    layers.Dense(1, activation='sigmoid') \n",
    "])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "scheduler = LearningRateScheduler(custom_scheduler)\n",
    "evaluate_given_keras_model(build_model,thresholds=[0.3, 0.5, 0.7], scheduler=scheduler)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
